[2026] Senior Machine Learning Engineer, Recommendation Systems - PhD Early Career

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Recommendation Systems are a key growth lever at Roblox, driving retention, engagement, and monetization for hundreds of millions of users. This role offers the unique opportunity to redefine how users search and discover everything from the most interesting immersive experiences and digital avatars in our Marketplace to personalized advertising. You will solve a diverse range of high-scale ranking, retrieval, and personalization problems across our platform. We combine cutting-edge research —including deep learning, generative AI, and reinforcement learning techniques— with large-scale engineering to bridge experimentation and production; you'll design algorithms that operate at massive scale and shape the next generation of recommender systems for user-generated content. Teams Hiring for This Role - Search and Discovery: powers major recommendation surfaces—conducting cutting-edge research in generative modeling, multimodal and MLLM technologies, designing advanced agentic AI algorithms to solve business requirements while achieving technical breakthroughs. - Safety, Alt Defense: architects a massive-scale detection engine that identifies recidivist bad actors across billions of accounts to ensure the long-term integrity of the Roblox community. You Will - Design and implement large-scale recommendation systems that power discovery across Roblox’s surfaces — experiences, avatars, and creator content. - Develop deep learning models for ranking, retrieval, and personalization using approaches in multimodal models, LLMs, and generative AI. - Collaborate with applied researchers, engineers, and product teams to advance experimentation and accelerate innovation. - Translate research into production systems that impact hundreds of millions of daily active users. - Work backward from user and product needs to deliver ML solutions that drive engagement, retention, and ecosystem growth. You Have - Possessing or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas. - Expertise in one or more areas: recommender systems, search systems, information retrieval, or generative models (e.g., LLMs, VLMs, VLAs) - Ability to design and architect systems for efficient personalization and user interest modeling using advanced attention mechanisms (e.g., sparse/linear attention). - A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues (e.g., SIGIR, KDD, RecSys, ICLR, ICML, NeurIPS) - Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems. Apply now to be considered for anticipated positions. You may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer. As you apply, you can find more information about our process by signing up for Speak_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process. For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page. Annual Salary Range $195,780—$242,100 USD Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted). Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic prote

Possessing or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas. Expertise in one or more areas: recommender systems, search systems, information retrieval, or generative models (e.g., LLMs, VLMs, VLAs). Ability to design and architect systems for efficient personalization and user interest modeling using advanced attention mechanisms (e.g., sparse/linear attention). A strong research track record, evidenced by multiple publications and presentations in top-tier, peer-reviewed venues (e.g., SIGIR, KDD, RecSys, ICLR, ICML, NeurIPS). Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.

Design and implement large-scale recommendation systems that power discovery across Roblox’s surfaces — experiences, avatars, and creator content. Develop deep learning models for ranking, retrieval, and personalization using approaches in multimodal models, LLMs, and generative AI. Collaborate with applied researchers, engineers, and product teams to advance experimentation and accelerate innovation. Translate research into production systems that impact hundreds of millions of daily active users. Work backward from user and product needs to deliver ML solutions that drive engagement, retention, and ecosystem growth.